Conventional forest inventory is still mostly based on field measurements. E.g. forest inventory at stand level, in which the forest is first divided into almost homogeneous compartments (typically of the size of 1-3 hectares), is presently based on plot measurement and visual observations. Aerial photographs and orthophotos (aerial images rectified to map projection) are generally used for the determination of stand boundaries and the walking route in the forest, but stand attributes, such as the volume (m3/ha), basal area (m2/ha, depicts the cross-sectional area per hectare corresponding to the trees cut at the height of 1.3 m), mean height (m), other basal area-type density attributes, tree species, age, and development class, are determined by measurements and assessments carried out in forests. This work has been tried to be made more effectively by increasing the level of automation, with field computers and with more automatic measurement equipment (see e.g. the Finnish patent FI 101016B). In forest inventory at stand level, the stand and tree attributes are calculated by plot wise information and visual observations. In addition to stand wise assessment, plot wise assessment, measurements of individual trees and estimation of larger areas, such as estimation of whole counties or parts of them, are carried out.
FI-patent 112402 presents a method, which is based on laser scanning and produces a three-dimensional presentation of the stand height in the forest. By applying pattern recognition methods to these height models, individual trees can be identified. The height, crown diameter and species can be derived for individual trees by means of the three-dimensional model. Other stand attributes are derived by means of this information. The advantage of the method is the very good accuracy (ca 10%) for the most important stand attributes (volume, basal area, mean height). The method requires, however, a considerably dense laser pulse amount for the separation of individual trees merely by means of laser information.
Earlier solutions are based on aerial image-derived canopy crown models combined with laser-derived digital terrain model (St. Onge and Achaia, Measuring forest canopy height using a combination of lidar and aerial photography data, http://larsees.geg.queensu.ca/lidar/publications/st-onge.pdf), identification of individual trees by using information from both aerial photos and laser (Leckie, Gougeon, Hill, Quinn, Amstrong, Shreenan, 2003, Combined high-density lidar and multispectral imagery for individual tree crown analysis, Can. J. Remote Sensing, Vol. 29, No. 5, pp. 633-649), connecting texture information from aerial images on to the laser models (Fujii, Arikawa, 2002, Urban Object reconstruction using airborne laser elevation image and aerial image, IEEE Transactions on Geosc. and Rem. Sens., Vol. 40, No. 10, pp. 2234-2240), segmentation of large forest areas and generalization of height information produced by laser to larger areas (Wulder ja Sheeman, 2003, Forest inventory height update through the integration of lidar data with segmented Landsat imagery, Can. J. Rem. Sens., Vol. 29, No. 5, pp. 536-543). Furthermore, the Finnish patent FI 112402 presents concepts for combining laser and aerial images for tree species production.